1. Field of the Invention
The invention relates to a medical image processing apparatus for detecting a candidate region of abnormal shadow by doing the image analysis on a medical image.
2. Description of Related Art
In a medical field, there is an occasion where a doctor interprets a medical image such as an X-ray image, an ultrasound image or the like, to find a lesion or to observe a course of a medical condition. So far, for the purpose of reducing burdens on doctor's interpretation of a medical image, the medical image processing apparatus called Computed-Aided Diagnosis (hereinafter, referred to as a CAD) which automatically detects shadow of a lesion as an abnormal shadow candidate by doing the image analysis on image data of the medical image is developed.
For the above-described CAD, a variety of algorithms are developed according to a type of abnormal shadow. For example, as a cancerous part of breast cancer, a type of abnormal shadow includes mass shadow, clustered-microcalcification shadow and the like. As an optimum algorithm for detecting mass shadow, a method using the Iris filter is suggested (see JP-Tokukaihei-10-91758A), and as an optimum algorithm for detecting clustered-microcalcification shadow, a method using the morphology filter is suggested.
Further, according to a type of shadow to be a detection target, CAD that is capable of selecting a detection algorithm of an abnormal shadow candidate is also developed (see JP-Tokukai-2002-112986A). In this case, as shown in FIG. 8, a detection is performed by each detection algorithm corresponding to a type of abnormal shadow, respectively.
However, if different types of shadow are overlapped in an image, for example, clustered-microcalcification shadow exists within mass shadow, since clustered-microcalcification shadow within mass shadow has little absorption difference of X rays compared to the case that the shadow exists in shadow of normal tissue such as a fat region or a mammary gland region, its contrast becomes extremely low. Further, in a region where different types of abnormal shadow are overlapped, quantum noise increases because the reached amount of X rays is low.
As descried above, although the contrast and the amount of noise in a region where different types of shadow are overlapped are different from these of the other regions, conventionally the detection of an abnormal shadow candidate is performed in all the image regions under the same detection condition. Therefore, abnormal shadow can not be distinguished within a region where different types of shadow are overlapped. As a result, the detection accuracy of an abnormal shadow candidate is low. Particularly, since the signal variance characteristic of noise and that of clustered-microcalcification resemble to each other, it is difficult to distinguish between the two, and there is a possibility of detecting a noise region by mistake.